Comparison of Bayesian network and binary Logistic Regression methods for prediction of prostate cancer

Prostate cancer is one of the most common cancers in men. Luckily, Serum PSA level, age, digital rectal examination (DRE), and clinical symptoms are helpful for early detection of this tumor. The aim of this study was to examine and compare the methods used for improving the diagnostic accuracy of serum PSA in Turkey, a country with low incidence of prostate cancer. The predictors used for early detection of prostatic carcinoma were identified by both Logistic Regression and Bayesian networks. The results of the methods were compared in terms of predicting performance and advantages.